{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a264eba-42cc-4800-8369-24b0891d167d",
   "metadata": {},
   "source": [
    "# Portfolio Optimization with the Quantum Approximate Optimization Algorithm (QAOA)\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Portfolio optimization [[1](#PortfolioWiki)] is the process of allocating a portfolio of financial assets optimally, according to some predetermined goal. Usually, the goal is to maximize the potential return while minimizing the financial risk of the portfolio. One can express this problem as a combinatorial optimization problem like many other real-world problems. In this demo, we'll show how the Quantum Approximate Optimization Algorithm (QAOA) [[2](#QAOA)] can be employed on the Classiq platform to solve the problem of portfolio optimization.\n",
    "\n",
    "### Modeling the Portfolio Optimization Problem\n",
    "\n",
    "As a first step, we have to model the problem mathematically. We will use a simple yet powerful model, which captures the essence of portfolio optimization:\n",
    "\n",
    "- A portfolio is built from a pool of $n$ financial assets, each asset labeled $i \\in \\{1,\\ldots,n\\}$.\n",
    "- Every asset's return is a random variable, with expected value $\\mu_i$ and variance $\\Sigma_i$ (modeling the financial risk involved in the asset).\n",
    "- Every two assets $i \\neq j$ have covariance $\\Sigma_{ij}$ (modeling market correlation between assets).\n",
    "- Every asset $i$ has a weight $w_i \\in D_i = \\{0,\\ldots,b_i\\}$ in the portfolio, with $b_i$ defined as the budget for asset $i$ (modeling the maximum allowed weight of the asset).\n",
    "- The return vector $\\mu$, the covariance matrix $\\Sigma$ and the weight vector $w$ are defined naturally from the above (with the domain $D = D_1 \\times D_2 \\times \\ldots \\times D_n$ for $w$).\n",
    "\n",
    "With the above definitions, the total expected return of the portfolio is $\\mu^T w$ and the total risk is $w^T \\Sigma w$. We'll use a simple difference of the two as our cost function, with the additional constraint that the total sum of assets does not exceed a predefined budget $B$. We note that there are many other possibilities for defining a cost function (e.g. add a scaling factor to the risk/return or even some non-linear relation). For reasons of simplicity we select the model below, and we assume all constants and variables are dimensionless.\n",
    "Thus, the problem is, given the constant inputs $\\mu, \\Sigma, D, B$, to find optimal variable $w$ as follows:\n",
    "\n",
    "$\\begin{equation*}\n",
    "\\min_{w \\in D}  w^T \\Sigma w - \\mu^T w,\n",
    "\\end{equation*}$\n",
    "subject to $\\Sigma_{i} w_i \\leq B$.\n",
    "\n",
    "The case presented above is called integer portfolio optimization, since the domains $D_i$ are over the (positive) integers.\n",
    "Another variation of this problem defines weights over binary domains, and will not be discussed here.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8abc22a4-3c75-4d1a-a77f-dee7f1ac3fe9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setup\n",
    "\n",
    "With the mathematical definition in place, we begin the implementation by importing necessary packages and classes. We will use the following external dependencies:\n",
    "\n",
    "1. NumPy\n",
    "2. Matplotlib\n",
    "3. Pyomo - a python framework for modeling optimization problems, which the Classiq platform uses as an interface to these types of problems.\n",
    "\n",
    "From the `classiq` package, we require classes related to combinatorial optimization and QAOA.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "952d49b3-5dc6-41a1-8822-0622df536cf7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:07.327339Z",
     "iopub.status.busy": "2024-05-07T15:06:07.326871Z",
     "iopub.status.idle": "2024-05-07T15:06:07.542423Z",
     "shell.execute_reply": "2024-05-07T15:06:07.541631Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pyomo.core as pyo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e9ec0e",
   "metadata": {},
   "source": [
    "# The Portfolio Optimization Problem Parameters\n",
    "\n",
    "First we define the parameters of the optimization problem, which include the expected return vector, the covariance matrix, the total budget and the asset-specific budgets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6212e51c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:07.547954Z",
     "iopub.status.busy": "2024-05-07T15:06:07.546656Z",
     "iopub.status.idle": "2024-05-07T15:06:07.552727Z",
     "shell.execute_reply": "2024-05-07T15:06:07.552195Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "returns = np.array([3, 4, -1])\n",
    "# fmt: off\n",
    "covariances = np.array(\n",
    "    [\n",
    "        [ 0.9,  0.5, -0.7],\n",
    "        [ 0.5,  0.9, -0.2],\n",
    "        [-0.7, -0.2,  0.9],\n",
    "    ]\n",
    ")\n",
    "# fmt: on\n",
    "total_budget = 6\n",
    "specific_budgets = [2, 2, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c181f72c-d233-4567-ae61-44f475689797",
   "metadata": {},
   "source": [
    "## The Pyomo Model for the Problem\n",
    "\n",
    "We proceed by defining the Pyomo model that will be used on the Classiq platform, using the problem parameters defined above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42650f31-8efe-4ca9-8ed6-f5d9d440bee4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:07.556775Z",
     "iopub.status.busy": "2024-05-07T15:06:07.555783Z",
     "iopub.status.idle": "2024-05-07T15:06:07.565348Z",
     "shell.execute_reply": "2024-05-07T15:06:07.564551Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "portfolio_model = pyo.ConcreteModel(\"portfolio_optimization\")\n",
    "num_assets = len(returns)\n",
    "\n",
    "# setting the variables\n",
    "portfolio_model.w = pyo.Var(\n",
    "    range(num_assets),\n",
    "    domain=pyo.Integers,\n",
    "    bounds=lambda _, idx: (0, specific_budgets[idx]),\n",
    ")\n",
    "w_array = list(portfolio_model.w.values())\n",
    "\n",
    "# setting the constraint\n",
    "portfolio_model.budget_rule = pyo.Constraint(expr=(sum(w_array) <= total_budget))\n",
    "\n",
    "# setting the expected return and risk\n",
    "portfolio_model.expected_return = returns @ w_array\n",
    "portfolio_model.risk = w_array @ covariances @ w_array\n",
    "\n",
    "# setting the cost function to minimize\n",
    "portfolio_model.cost = pyo.Objective(\n",
    "    expr=portfolio_model.risk - portfolio_model.expected_return, sense=pyo.minimize\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c671eeac-5b61-4ab4-9e92-cfcb2ed8170b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setting Up the Classiq Problem Instance\n",
    "\n",
    "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c044e30f-2b4f-41ef-9bc0-11b951cb88db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:07.570439Z",
     "iopub.status.busy": "2024-05-07T15:06:07.569306Z",
     "iopub.status.idle": "2024-05-07T15:06:10.280055Z",
     "shell.execute_reply": "2024-05-07T15:06:10.275821Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce2689a-d47f-42c0-9468-5faa4da21d20",
   "metadata": {},
   "source": [
    "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c4a6a91-aece-4a3b-9fa2-e7f76c90f296",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:10.287616Z",
     "iopub.status.busy": "2024-05-07T15:06:10.287145Z",
     "iopub.status.idle": "2024-05-07T15:06:10.292183Z",
     "shell.execute_reply": "2024-05-07T15:06:10.291510Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78aeea0-246b-4a58-9d7f-94cded812348",
   "metadata": {},
   "source": [
    "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3988443-adff-4196-981f-d22307de17c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:10.297071Z",
     "iopub.status.busy": "2024-05-07T15:06:10.295820Z",
     "iopub.status.idle": "2024-05-07T15:06:12.171845Z",
     "shell.execute_reply": "2024-05-07T15:06:12.171188Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=portfolio_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d64c135-ec3b-490e-ae60-2d72f0ff633c",
   "metadata": {},
   "source": [
    "We also set the quantum backend we want to execute on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0b0013d-5152-4221-a8d5-d35820c3a878",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:12.174681Z",
     "iopub.status.busy": "2024-05-07T15:06:12.174351Z",
     "iopub.status.idle": "2024-05-07T15:06:12.190978Z",
     "shell.execute_reply": "2024-05-07T15:06:12.190335Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "804e8e99-2e63-43df-9168-f1cb8497bdad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:12.193308Z",
     "iopub.status.busy": "2024-05-07T15:06:12.192932Z",
     "iopub.status.idle": "2024-05-07T15:06:12.333982Z",
     "shell.execute_reply": "2024-05-07T15:06:12.333340Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"portfolio_optimization\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b098aa8a-e47f-474a-b5a4-75f3b98d2628",
   "metadata": {},
   "source": [
    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
    "\n",
    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73cccd8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:12.336365Z",
     "iopub.status.busy": "2024-05-07T15:06:12.336172Z",
     "iopub.status.idle": "2024-05-07T15:06:14.856707Z",
     "shell.execute_reply": "2024-05-07T15:06:14.855692Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/5c17bd7f-2b65-4deb-94a5-6fb8708345d1?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import show, synthesize\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45f19792-d1ec-48b4-bc15-0fe881e48cd9",
   "metadata": {},
   "source": [
    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6607c43-7b33-44dd-9e38-b90c2db888e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:14.861747Z",
     "iopub.status.busy": "2024-05-07T15:06:14.861264Z",
     "iopub.status.idle": "2024-05-07T15:06:18.624172Z",
     "shell.execute_reply": "2024-05-07T15:06:18.623365Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c622a1-d8ae-47ac-a924-86d5b73bbd45",
   "metadata": {},
   "source": [
    "We can check the convergence of the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7cff5dd0-312b-45b3-8af3-0fd6fa04b6e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:18.628597Z",
     "iopub.status.busy": "2024-05-07T15:06:18.628037Z",
     "iopub.status.idle": "2024-05-07T15:06:18.667398Z",
     "shell.execute_reply": "2024-05-07T15:06:18.666685Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "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",
      "image/png": "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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a26d5c-ffc0-40bc-9964-e9fb6e16f232",
   "metadata": {},
   "source": [
    "And print the optimization results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e5789ba-f1a5-4108-a0fa-a1b90870da1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:18.670930Z",
     "iopub.status.busy": "2024-05-07T15:06:18.670670Z",
     "iopub.status.idle": "2024-05-07T15:06:19.843274Z",
     "shell.execute_reply": "2024-05-07T15:06:19.842574Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.003</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>[1, 2, 1]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.003</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>[1, 2, 1]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.006</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>[2, 1, 1]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.010</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>[2, 1, 1]</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0.002</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>[1, 2, 1]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     probability  cost   solution  count\n",
       "98         0.003  -4.8  [1, 2, 1]      3\n",
       "141        0.003  -4.8  [1, 2, 1]      3\n",
       "35         0.006  -4.8  [2, 1, 1]      6\n",
       "5          0.010  -4.8  [2, 1, 1]     10\n",
       "210        0.002  -4.8  [1, 2, 1]      2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    portfolio_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05449034-19e5-4b5d-80ea-7b2ba7ccd877",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:19.845777Z",
     "iopub.status.busy": "2024-05-07T15:06:19.845472Z",
     "iopub.status.idle": "2024-05-07T15:06:19.849615Z",
     "shell.execute_reply": "2024-05-07T15:06:19.848912Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [2, 1, 1] , cost = -4.800000000000001\n"
     ]
    }
   ],
   "source": [
    "idx = optimization_result.cost.idxmin()\n",
    "print(\n",
    "    \"x =\", optimization_result.solution[idx], \", cost =\", optimization_result.cost[idx]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b170b0ef-1e3f-4680-a7d4-82b4e537d785",
   "metadata": {},
   "source": [
    "And the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f48034c6-8000-4bcf-83b0-e3a76a38d9c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:19.851995Z",
     "iopub.status.busy": "2024-05-07T15:06:19.851643Z",
     "iopub.status.idle": "2024-05-07T15:06:20.087635Z",
     "shell.execute_reply": "2024-05-07T15:06:20.086888Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c093ae6",
   "metadata": {},
   "source": [
    "Lastly, we can compare to the classical solution of the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "324c9a09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:06:20.092527Z",
     "iopub.status.busy": "2024-05-07T15:06:20.091284Z",
     "iopub.status.idle": "2024-05-07T15:06:20.178692Z",
     "shell.execute_reply": "2024-05-07T15:06:20.177959Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model portfolio_optimization\n",
      "\n",
      "  Variables:\n",
      "    w : Size=3, Index=w_index\n",
      "        Key : Lower : Value              : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 : 1.9999999999999998 :     2 : False : False : Integers\n",
      "          1 :     0 : 1.0000000000000002 :     2 : False : False : Integers\n",
      "          2 :     0 : 1.0000000000000002 :     2 : False : False : Integers\n",
      "\n",
      "  Objectives:\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Value\n",
      "        None :   True :  -4.8\n",
      "\n",
      "  Constraints:\n",
      "    budget_rule : Size=1\n",
      "        Key  : Lower : Body : Upper\n",
      "        None :  None :  4.0 :   6.0\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(portfolio_model)\n",
    "\n",
    "portfolio_model.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ac0d67",
   "metadata": {},
   "source": [
    "We can see that most of the solutions obtained by running QAOA are close to the minimal solution obtained classically, demonstrating the effectiveness of the algorithm. Also, note the non-trivial solution which includes a non-zero weight for the asset with negative expected return, demonstrating that it sometimes makes sense to include such assets in the portfolio as a risk-mitigation strategy - especially if they are highly anti-correlated with the rest of the assets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61370cf4",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "\n",
    "## References\n",
    "\n",
    "<a id='PortfolioWiki'>[1]</a>: [Portfolio Optimization (Wikipedia)](https://en.wikipedia.org/wiki/Portfolio_optimization)\n",
    "\n",
    "<a id='QAOA'>[2]</a>: [Farhi, Edward, Jeffrey Goldstone, and Sam Gutmann. \"A quantum approximate optimization algorithm.\" arXiv preprint arXiv:1411.4028 (2014).](https://arxiv.org/abs/1411.4028)\n",
    "\n",
    "<a id='cvar'>[3]</a>: [Barkoutsos, Panagiotis Kl, et al. \"Improving variational quantum optimization using CVaR.\" Quantum 4 (2020): 256.](https://arxiv.org/abs/1907.04769)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "a07aacdcc8a415e7643a2bc993226848ff70704ebef014f87460de9126b773d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
